Analysis of Video Games Sales

Content

• Introduction. Video Game Industry

• Data description and objectives

• Data validation and manipulation

• Data visualization

• Conclusion

1. Introduction. Video Game Industry

Today, the video game industry is one of the largest in the entertainment industry. Its capitalization in 2021 reached more than USD 190 billion with an increase of more than 9% per year. The expected value of the industry by 2026 is estimated at USD 314 billion. Every year the video game industry is growing more and more. But it was not always so.

Initially, in the mid-70s, it was a small community of enthusiasts. But over the years it has increased more and more, and with it the profit has grown. From low-profit projects over several decades, the total annual profit has grown to USD 9.5 billion, and in 2021 this figure was USD 60 billion. Every year, a large number of new projects come out, both from large and influential companies, as well as from indie developers. The variety of games attracts more and more people. The availability of games also contributes to this. Even on non-gaming computers, you can run indie games. On the road, you can always kill time in arcade games. In general, over time, gaming has become ubiquitous.

This project will provide the analysis of Video Game sales from 1980 to 2020 in 3 different regions.

2. Data description and objectives

This dataset was scrapped from https://www.vgchartz.com/. It contains a list of video games from 1980 to 2020 with more than 100,000 saled copies. Total number of this records is 16,598. Below is attributes we will use in our analysis:

• Rank - Ranking of overall sales
• Name - Game title
• Platform - Platform on which the game was released
• Year - Year of the game's release
• Genre - Genre of the game
• Publisher - Publisher of the game
• NA_Sales - Sales in North America (in millions)
• EU_Sales - Sales in Europe (in millions)
• JP_Sales - Sales in Japan (in millions)
• Other_Sales - Sales in the rest of the world (in millions)
• Global_Sales - Total worldwide sales

Objectives

For this project, analysis and visualization consists of 4 parts:

• Game prevalance in regions over years

• Number of released games every year

• Most popular genres in regions

• Top 20 Game Publishers of all timee

3. Data validation and manipulation

For easier and clean work with data we will make some manipulation:

• Drop unnecessary columns
• Rename column for better visualization
• Determine how many missing values
• Change type of necessary columns

# Importing necessary libraries
import pandas as pd
import numpy as np

# Reading our data frame from csv file
df = pd.read_csv('vgsales.csv')
df.head(10)

	Rank	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
0	1	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	2	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	3	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	4	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	5	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37
5	6	Tetris	GB	1989.0	Puzzle	Nintendo	23.20	2.26	4.22	0.58	30.26
6	7	New Super Mario Bros.	DS	2006.0	Platform	Nintendo	11.38	9.23	6.50	2.90	30.01
7	8	Wii Play	Wii	2006.0	Misc	Nintendo	14.03	9.20	2.93	2.85	29.02
8	9	New Super Mario Bros. Wii	Wii	2009.0	Platform	Nintendo	14.59	7.06	4.70	2.26	28.62
9	10	Duck Hunt	NES	1984.0	Shooter	Nintendo	26.93	0.63	0.28	0.47	28.31

# Shape of data frame
df.shape

(16598, 11)

# In this data analysis we won't use Rank feature, so we will drop it
df = df.drop('Rank', axis = 1)
df.head()

	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
0	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37

# Renaming Sales columns to better look visualization
df.columns = df.columns.str.replace('_', ' ')
df.head()

	Name	Platform	Year	Genre	Publisher	NA Sales	EU Sales	JP Sales	Other Sales	Global Sales
0	Wii Sports	Wii	2006.0	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	Super Mario Bros.	NES	1985.0	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	Mario Kart Wii	Wii	2008.0	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	Wii Sports Resort	Wii	2009.0	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37

# Checkout missing values
# As we can see there are missing values in Year and Publisher columns
# There are not so many rows to have an impact on statistics, so we will delete them
df.isna().sum()

Name              0
Platform          0
Year            271
Genre             0
Publisher        58
NA Sales          0
EU Sales          0
JP Sales          0
Other Sales       0
Global Sales      0
dtype: int64

# Delete rows with null values
df = df.dropna()

# Changing Year column data type to int for better visualization
df['Year'] = df['Year'].astype(int)
df.head()

	Name	Platform	Year	Genre	Publisher	NA Sales	EU Sales	JP Sales	Other Sales	Global Sales
0	Wii Sports	Wii	2006	Sports	Nintendo	41.49	29.02	3.77	8.46	82.74
1	Super Mario Bros.	NES	1985	Platform	Nintendo	29.08	3.58	6.81	0.77	40.24
2	Mario Kart Wii	Wii	2008	Racing	Nintendo	15.85	12.88	3.79	3.31	35.82
3	Wii Sports Resort	Wii	2009	Sports	Nintendo	15.75	11.01	3.28	2.96	33.00
4	Pokemon Red/Pokemon Blue	GB	1996	Role-Playing	Nintendo	11.27	8.89	10.22	1.00	31.37

# As we can see there are too few values in 2016, 2017, 2020. So we will delete them
df.groupby('Year').sum().tail()

	NA Sales	EU Sales	JP Sales	Other Sales	Global Sales
Year
2014	131.97	125.63	39.46	40.02	337.03
2015	102.82	97.71	33.72	30.01	264.44
2016	22.66	26.76	13.67	7.75	70.90
2017	0.00	0.00	0.05	0.00	0.05
2020	0.27	0.00	0.00	0.02	0.29

# We will rewrite all years, except 2016, 2017 and 2020
df = df[(df['Year'] != 2016) & (df['Year'] != 2017) & (df['Year'] != 2020)]
df.groupby('Year').sum().tail()

	NA Sales	EU Sales	JP Sales	Other Sales	Global Sales
Year
2011	241.00	167.31	53.04	54.39	515.80
2012	154.93	118.76	51.74	37.82	363.49
2013	154.77	125.80	47.59	39.82	368.11
2014	131.97	125.63	39.46	40.02	337.03
2015	102.82	97.71	33.72	30.01	264.44

4. Data visualization

Before start, we shoud import all needed libraries.

# For data visualization we will use matplotlib and plotly libraries
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from ipywidgets import interact

# Define style and size of our plots.
plt.style.use('ggplot')
plt.rc('figure', figsize = (16, 9))
plt.rc('font', size = 14)

4.1 Game prevalance in regions over years

# Preparing data frame for visualization
game_prevalance = df.groupby('Year').sum().drop('Global Sales', axis = 1)
game_prevalance.head()

	NA Sales	EU Sales	JP Sales	Other Sales
Year
1980	10.59	0.67	0.00	0.12
1981	33.40	1.96	0.00	0.32
1982	26.92	1.65	0.00	0.31
1983	7.76	0.80	8.10	0.14
1984	33.28	2.10	14.27	0.70

# Making a plot for all regions with loop
for i in game_prevalance.columns:
    plt.plot(game_prevalance[i], label = i.split()[0], linewidth = 3.0)

plt.title('Figure 1.1 Game Prevalance over years', fontsize = 18)
plt.xlabel('Year', fontsize = 16)
plt.ylabel('Sales (in millions)', fontsize = 16)
plt.legend(fontsize = 16, loc = 'upper left')

plt.show()

As we can see on Figure 1.1, North America region have a majority in game sales all over the time. And the first game sales accounted for it. The first significant jump was in 1995 for NA, after that in 2000 massive increase for both NA and EU regions. This brings us to the fact that trend for NA and EU is similar. On the other side there are sales in Japan and other world. Graph of the other regions after 2005 become more like trends in NA and EU, but the JP has another trends which we will see later in our analysis.

Now we will look a bar plot representation for game prevalance:

# Preparing data frame for visualization
prevalance_seg = df.groupby((df['Year']//5)*5).sum().drop(columns = {'Year', 'Global Sales'}, axis = 1)
prevalance_seg

	NA Sales	EU Sales	JP Sales	Other Sales
Year
1980	111.95	7.18	22.37	1.59
1985	123.71	24.02	80.12	5.54
1990	115.36	42.82	117.89	6.88
1995	460.75	240.05	254.44	40.54
2000	897.05	467.11	200.24	134.91
2005	1506.17	786.97	310.45	329.72
2010	986.91	714.07	251.32	231.95
2015	102.82	97.71	33.72	30.01

# Creating bar plot
fig = px.bar(prevalance_seg, x = prevalance_seg.index, y = prevalance_seg.columns,
             labels = {
                     'value' : 'Sales(in millions)',
                     'variable' : 'Region'
                 },
             title = 'Figure 1.2 Game Prevalance every 5 years')
fig['layout']['font'] = dict(size=13)
fig.show()

For this graph we combined our Year feature to a periods of time. We will divide it by 7 periods of 5 years. We could it on figure 1.2. From this bar chart it becomes clear that first game sales was not only for NA region, but also for JP in less amount of game copies. In another 5 Japan sales approaching to NA and Europe region also started to sale games. The significant changes we could see from 1995 to 2000 as like as we saw in Figure 1.1. And the peak of sales was from 2005 to 2010, then in the periods between 2010 and 2015 sales significantly goes down.

4.2 Number of released games every year

# For this visualisation we will need only Year and number of our games
released_games = df.groupby('Year').count().iloc[:, :1]
released_games = released_games.rename(columns = {'Name':'Released'})
released_games.head(10)

	Released
Year
1980	9
1981	46
1982	36
1983	17
1984	14
1985	14
1986	21
1987	16
1988	15
1989	17

# For visualization we will create a bar chart 
plt.bar(released_games.index, released_games['Released'], color = '#2dd4ed', edgecolor = 'black')

plt.xlabel('Year', fontsize = 16)
plt.ylabel('Released games', fontsize = 16)
plt.title('Figure 2. Number of games released every year', fontsize = 18)

plt.show();

Here on Figure 2 we can see that as the game industry grew many companies started to make more games. Also we can see that the first increasing of game releases started in 1995, then in 2003 and peak of released games was in 2008 and 2009. If we compare this graph(Figure 2) with the Game prevalance graph(Figure 1.1) we could see that they have a similar trend.

4.3 Most popular genres in regions

# Preparing data frame for visualization
genres_in_region = df.groupby('Genre').sum().drop(columns = {'Year', 'Global Sales'}, axis = 1)
genres_in_region

	NA Sales	EU Sales	JP Sales	Other Sales
Genre
Action	855.90	510.12	152.86	183.09
Adventure	101.59	63.35	51.04	16.59
Fighting	219.14	98.85	86.51	35.73
Misc	396.70	211.68	105.86	73.89
Platform	445.20	199.78	130.54	51.20
Puzzle	122.01	50.52	56.68	12.47
Racing	356.60	235.17	56.60	76.49
Role-Playing	325.11	186.28	346.62	58.94
Shooter	567.72	302.75	37.57	99.48
Simulation	181.51	112.93	63.24	31.34
Sports	665.52	363.98	133.98	130.73
Strategy	67.72	44.52	49.05	11.19

def x(Region):
    
    # Preparing data frame for specific region
    genre_df = genres_in_region.sort_values(Region, ascending = True)
    reg = Region.split()[0]
    
    # Setting colors for different regions
    if reg == 'NA':
        color = '#4446cf'
    elif reg == 'EU':
        color = '#8a44cf'
    elif reg =='JP':
        color = '#cf446e'
    else:
        color = '#448ccf'
        
    # Creating bar plot with sorting by amount of sales
    plt.barh(genre_df.index, 
             genre_df[Region], 
             color = color, edgecolor = 'black')
    
    # Setting label and title
    plt.xlabel('Sales (in millions)', fontsize = 16)
    plt.title(f'Figure 3. Most popular genres in {reg}', fontsize = 18)
    
    plt.show()
    
interact(x, Region = genres_in_region.columns);

On figure 3 we visualized 4 our regions to see what genres are preferred by gamers of this region. Comparing regions between each other brings us to the fact that similarity with NA, EU and other region is high, on the other side is JP with a absolutely different trend. The top 3 game genres for NA, EU and other world is: Action, Sports and Shooters. Other games have a different places, but general trend is the same.

In Japan most popular genre is RPG, and its dominance on the market clearly visible. RPG sales in JP is more than sales of second top genre in 2 times. Also in this region least popular genre is shooters. But if we look at the other regions shooters is a top 3 genre. We could explain this that Japanese culture very different and the games they prefer also differs.

4.4 Top 20 Game Publishers of all time

# Data frame we will use in this visualisation
pr = df.groupby('Publisher').sum().drop('Year', axis = 1).sort_values('Global Sales', ascending = False)
pr.head(20)

	NA Sales	EU Sales	JP Sales	Other Sales	Global Sales
Publisher
Nintendo	814.59	417.37	453.82	94.99	1780.96
Electronic Arts	580.58	360.47	13.89	126.00	1081.14
Activision	424.34	212.33	6.40	74.35	717.74
Sony Computer Entertainment	262.79	184.68	73.88	79.35	600.72
Ubisoft	248.69	158.65	7.09	48.86	463.49
Take-Two Interactive	218.64	117.33	5.81	54.76	396.41
THQ	208.60	94.60	5.01	32.11	340.44
Konami Digital Entertainment	88.90	68.14	90.30	29.84	277.35
Sega	108.65	81.27	55.70	24.27	269.89
Namco Bandai Games	67.38	41.03	124.50	14.06	247.16
Microsoft Game Studios	154.69	68.10	3.11	18.45	244.37
Capcom	77.62	38.65	66.85	14.58	197.83
Atari	101.23	25.78	10.70	8.73	146.75
Square Enix	48.12	31.90	47.56	13.67	141.20
Warner Bros. Interactive Entertainment	73.97	47.15	1.02	16.69	138.82
Disney Interactive Studios	70.44	34.36	0.56	13.15	118.76
Eidos Interactive	47.85	34.85	6.11	7.90	96.75
LucasArts	48.43	26.00	0.20	10.28	84.95
Bethesda Softworks	38.49	29.49	1.42	9.81	79.28
Midway Games	45.10	18.22	0.12	5.69	69.29

def x(Region):
    
    # Preparing data frame for specific region
    pub_in_region = df.groupby('Publisher').sum().drop('Year', axis = 1).sort_values(Region, ascending = False).head(20)
    
    # Creation a plot
    fig = px.bar(pub_in_region, x = pub_in_region.index, y = Region, 
                 labels = {
                     Region : 'Sales(in millions)'
                 },
                 title = f'Figure 4. Top 20 Game Publishers of all time in {Region.split()[0]}')
    fig['layout']['font'] = dict(size=13)
    fig.show()
    
interact(x, Region = pr.columns);

This bar plot shows us a top 20 game publishers of all time by sold games in 4 different regions. Also, there is a possibility to see a global statistics. From the Figure 4 we can say, that most popular publisher is surely japanese company Nintendo. The trend of all of the regions except Japan is almost the same. But again the JP region have a completely different favorite Publishers. Top 7 Publishers in JP chart is Japanese companies. And if everything with Nintendo is clear, the situation with other companies is brings us to the fact, that Japanese Publishers is more like to release a game that will be mostly bought in JP region than in other.

5. Conclusion

Based on the analysis we made, we can conclude several things:

• Game prevalance increased greatly over the years. Most significant jump was in 2000 and in 2008 the market started to going down.
• Number of released games every year increased as much as more games saled.
• Popular genres in all region except Japan is similar. JP region have a completely different genre taste.
• The most popular game publisher in the world is Nintendo. Trend of top 20 publishers is general for all regions except JP.

Summing up this points, we could say that a general trend on the games more-less the same for all region except Japan. We assume that due to difference between Eastern and Western culture there is also a difference in a game they preffer. It's more likely that japanese gamer will buy a game that released by japanese publisher. According to our analysis of the most popular genres in regions, we can see that Japanese mostly preferes RPGs games. Due to this japanese Publishers is more interested in releasing a RPGs to satisfy their local market which is being their main.